{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# WEEK02\n",
    "## 熊猫(pandas)简介\n",
    "* Pandas 有三种基本数据结构：Series、DataFrame 和 Index。\n",
    "       \n",
    "![02_io_readwrite](https://pandas.pydata.org/pandas-docs/version/1.0.2/_images/02_io_readwrite.svg)\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "## 熊猫处理什么样的数据？\n",
    "要将数据手动存储在表中，请创建一个DataFrame。使用Python的列表字典时，字典键将用作列标题，而每个列表中的值将用作的行DataFrame\n",
    "\n",
    "> <mark>框框框</mark>，探索，清理和处理数据在Pandas中，数据表称为DataFrame\n",
    "\n",
    "\n",
    "对数据科学家来说: \n",
    "\n",
    "* 竖着的列column通常放**变数variables**\n",
    "* 横着的行row通常放**观察observations**\n",
    "\n",
    "对数据及信息管理人员来说:\n",
    "* 表格数据（例如存储在电子表格或数据库中的数据）是很常见的，最主流的数据结构和查询语言是SQL\n",
    "* 树状文本数据（例如HTML, XML, JSON数据）是很常见的，HTML/XML最主流的查询语言是xpath\n",
    "![01_table_dataframe.svg](https://pandas.pydata.org/pandas-docs/version/1.0.2/_images/01_table_dataframe.svg)\n",
    "----- \n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 框框框的代码片语\n",
    "**注意标点及缩进**\n",
    "\n",
    "```python\n",
    "框框 = pd.DataFrame ( {\n",
    "        \"变数X\": [\"观察X1\", \"观察X2\", \"观察X3\", \"观察X4\"],\n",
    "        \"变数Y\": [\"观察Y1\", \"观察Y2\", \"观察Y3\", \"观察Y4\"],\n",
    "        \"变数Z\": [\"观察Z1\", \"观察Z2\", \"观察Z3\", \"观察Z4\"],\n",
    "      } )```\n",
    "\n",
    "记得，像人类语言一样，说的清楚，人就可以读的比较清楚....\n",
    "\n",
    "* pd.DataFrame 的主流参数是字典\n",
    "* 该字典的键keys是由变数构成，相当於表格中一行行的标题\n",
    "* 该字典的值values是由观察的列表构成，相当於表格中一行行的数据\n",
    "* 表格真的要是表格, 该字典的每个观察的列表数量必需齐一\n",
    "\n",
    "[Q] 最后一个逗点可有可无，在pandas情境下最好留(为什麽?)\n",
    "\n",
    "[A]结尾行不用逗号，但最好留下，因为之后往下添加内容时不会忘记"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>姓名</th>\n",
       "      <th>年龄</th>\n",
       "      <th>性别</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>小红</td>\n",
       "      <td>18</td>\n",
       "      <td>女</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>小黄</td>\n",
       "      <td>19</td>\n",
       "      <td>男</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>小黑</td>\n",
       "      <td>20</td>\n",
       "      <td>男</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>小蓝</td>\n",
       "      <td>21</td>\n",
       "      <td>女</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   姓名  年龄 性别\n",
       "0  小红  18  女\n",
       "1  小黄  19  男\n",
       "2  小黑  20  男\n",
       "3  小蓝  21  女"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#### 练习A1-框框框的课堂建构\n",
    "# A1\n",
    "框框 = pd.DataFrame ( {\n",
    "        \"姓名\": [\"小红\", \"小黄\", \"小黑\", \"小蓝\"],\n",
    "        \"年龄\": [\"18\", \"19\", \"20\", \"21\"],\n",
    "        \"性别\": [\"女\", \"男\", \"男\", \"女\"],\n",
    "      } )\n",
    "框框\n",
    "# print (\"做别的事去了\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>姓名</th>\n",
       "      <th>年龄</th>\n",
       "      <th>性别</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>小红</td>\n",
       "      <td>18</td>\n",
       "      <td>女</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>小黄</td>\n",
       "      <td>19</td>\n",
       "      <td>男</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>小黑</td>\n",
       "      <td>20</td>\n",
       "      <td>男</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>小蓝</td>\n",
       "      <td>21</td>\n",
       "      <td>女</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   姓名  年龄 性别\n",
       "0  小红  18  女\n",
       "1  小黄  19  男\n",
       "2  小黑  20  男\n",
       "3  小蓝  21  女"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# A1 dict\n",
    "# 先弄字典, 再弄 框框 的写法片语\n",
    "字典 = {\n",
    "       \"姓名\": [\"小红\", \"小黄\", \"小黑\", \"小蓝\"],\n",
    "        \"年龄\": [\"18\", \"19\", \"20\", \"21\"],\n",
    "        \"性别\": [\"女\", \"男\", \"男\", \"女\"],\n",
    "       }\n",
    "\n",
    "框框 = pd.DataFrame ( 字典 )\n",
    "框框"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   姓名  年龄 性别\n",
      "0  小红  18  女\n",
      "1  小黄  19  男\n",
      "2  小黑  20  男\n",
      "3  小蓝  21  女\n"
     ]
    }
   ],
   "source": [
    "# A1 print打印\n",
    "# print是文字text输出为主，没有表格\n",
    "print (框框)  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>姓名</th>\n",
       "      <th>年龄</th>\n",
       "      <th>性别</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>小红</td>\n",
       "      <td>18</td>\n",
       "      <td>女</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>小黄</td>\n",
       "      <td>19</td>\n",
       "      <td>男</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>小黑</td>\n",
       "      <td>20</td>\n",
       "      <td>男</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>小蓝</td>\n",
       "      <td>21</td>\n",
       "      <td>女</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   姓名  年龄 性别\n",
       "0  小红  18  女\n",
       "1  小黄  19  男\n",
       "2  小黑  20  男\n",
       "3  小蓝  21  女"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# A1 display\n",
    "# 在ipynb可用 display 比较上看\n",
    "\n",
    "from IPython.display import display, HTML\n",
    "# 從 IPython.display 模塊 導入使用 display和HTML\n",
    "display (框框)\n",
    "# 与print相比多了表格"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Series 对象\n",
    "* Pandas 的 Series 对象是一个带索引数据构成的一维数组。Series 对象将一组数据和一组索引绑定在一起，我们可以通过 values 属性和 index 属性获取数据。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "#### 练习A2-**框框框**(DataFrame)的取变数成 **系列** (Series)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    小红\n",
       "1    小黄\n",
       "2    小黑\n",
       "3    小蓝\n",
       "Name: 姓名, dtype: object"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# A2 Series\n",
    "# first slice\n",
    "框框 [\"姓名\"]  # [] 从表格中取出这部分内容"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['18', '19', '20', '21'], dtype=object)"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# A2 Series values\n",
    "框框 [\"年龄\"].values # 取表格中这部分值"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RangeIndex(start=0, stop=4, step=1)"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# A2 Series index\n",
    "框框 [\"年龄\"].index # 索引"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "姓名    小蓝\n",
       "年龄    21\n",
       "性别     女\n",
       "Name: 3, dtype: object"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# A2 DataFrame .loc[]\n",
    "# 用 .loc[] 取列, 相当於所有变数的某一次观察\n",
    "框框.loc[3]   # loc = location  row"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['小红', '小黄', '小黑', '小蓝']\n",
      "['小红', '小黄', '小黑', '小蓝']\n",
      "['小红', '小黄', '小黑', '小蓝']\n",
      "[0, 1, 2, 3]\n"
     ]
    }
   ],
   "source": [
    "# A2 Series to_list()\n",
    "# 从表格中取出想要部分的方法总结\n",
    "# 降阶打击, 变列表的方法\n",
    "print ( 框框 [\"姓名\"].to_list() )    \n",
    "print ( list(框框 [\"姓名\"]) )\n",
    "print ( list(框框 [\"姓名\"].values) )\n",
    "print ( list(框框 [\"姓名\"].index))\n",
    "            "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "### 框框框的小结\n",
    "\n",
    "1. DataFrame有2个常用属性，分别是index 属性和columns 属性。前者可以获取索引标签（行标签01234）；后者是是存放列标签的Index 对象（姓名）。\n",
    "2. DataFrame 是特殊的字典，一列映射一个Series 的数据。\n",
    "3. 框框框的字典取的是某个变数（也就是一列的变数）所有观察, 用.loc[] 取个次观察的所有变数（一行的变数）\n",
    "\n",
    "\n",
    "----- \n",
    "----- "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![02_io_readwrite.svg](https://pandas.pydata.org/pandas-docs/version/1.0.2/_images/02_io_readwrite.svg)\n",
    "## 如何读写表格数据？\n",
    "读读读，写写写，数据科学家不想浪费时间编程去处理不同数据格式，所以pandas集成了常用的现成的文件格式或数据源（csv，excel，sql，json，parquet等）..."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "#### 读读读的代码片语操练 "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 读读读的代码片语\n",
    "**注意标点及缩进**\n",
    "\n",
    "```python\n",
    "\n",
    "读到csv = pd.read_csv(\"路径档案名\", encoding=\"utf8\")\n",
    "读到tsv = pd.read_csv(\"路径档案名\", encoding=\"utf8\", sep=\"\\t\")\n",
    "读到excel = pd.read_excel(\"路径档案名\", encoding=\"utf8\", sheet_name=\"分页名称\")\n",
    "```\n",
    "\n",
    "代码片语说明\n",
    "\n",
    "* pandas提供了read_csv()将存储为csv文件的数据读入pandas的功能DataFrame。pandas支持许多不同的文件格式或数据源（csv，excel，sql，json，parquet等），每种格式都有前缀read_*。\n",
    "* 读取数据后，请确保始终对数据进行检查。当显示时DataFrame，默认情况下将显示前5行\n",
    "* 要查看前N行DataFrame，请使用head(N)具有所需行数作为参数的方法。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 总结\n",
    "#  df.head(n)看前n行\n",
    "#  df.info()查看索引、数据类型和内存信息\n",
    "#  df.shape()查看行数和列数\n",
    "#  df.describe(include=\"all\")查看数值型列的汇总统计\n",
    "#  df.to_markdown()输出md\n",
    "#  df.to_html()\n",
    "#  df.to_json()\n",
    "#  df.to_dict()\n",
    "#  df.to_sql()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>排名</th>\n",
       "      <th>企业名称</th>\n",
       "      <th>Company Name</th>\n",
       "      <th>估值（亿人民币）</th>\n",
       "      <th>国家</th>\n",
       "      <th>城市</th>\n",
       "      <th>行业</th>\n",
       "      <th>掌门人/创始人</th>\n",
       "      <th>成立年份</th>\n",
       "      <th>部分投资机构</th>\n",
       "      <th>region</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>蚂蚁金服</td>\n",
       "      <td>Ant Financial</td>\n",
       "      <td>10000</td>\n",
       "      <td>中国</td>\n",
       "      <td>杭州</td>\n",
       "      <td>金融科技</td>\n",
       "      <td>井贤栋</td>\n",
       "      <td>2014</td>\n",
       "      <td>春华资本、中投海外、红杉资本</td>\n",
       "      <td>环杭州湾大湾区</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>字节跳动</td>\n",
       "      <td>Bytedance</td>\n",
       "      <td>5000</td>\n",
       "      <td>中国</td>\n",
       "      <td>北京</td>\n",
       "      <td>媒体和娱乐</td>\n",
       "      <td>张一鸣</td>\n",
       "      <td>2012</td>\n",
       "      <td>红杉资本、海纳亚洲、纪源资本、启明创投</td>\n",
       "      <td>渤海大湾区</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>滴滴出行</td>\n",
       "      <td>Didi Chuxing</td>\n",
       "      <td>3600</td>\n",
       "      <td>中国</td>\n",
       "      <td>北京</td>\n",
       "      <td>共享经济</td>\n",
       "      <td>程维</td>\n",
       "      <td>2012</td>\n",
       "      <td>腾讯、阿里巴巴、红杉资本、经纬中国、纪源资本</td>\n",
       "      <td>渤海大湾区</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>6</td>\n",
       "      <td>陆金所</td>\n",
       "      <td>Lufax</td>\n",
       "      <td>2700</td>\n",
       "      <td>中国</td>\n",
       "      <td>上海</td>\n",
       "      <td>金融科技</td>\n",
       "      <td>计葵生</td>\n",
       "      <td>2011</td>\n",
       "      <td>摩根士丹利、中银集团、国泰君安（香港）</td>\n",
       "      <td>环杭州湾大湾区</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>11</td>\n",
       "      <td>微众银行</td>\n",
       "      <td>WeBank</td>\n",
       "      <td>1500</td>\n",
       "      <td>中国</td>\n",
       "      <td>深圳</td>\n",
       "      <td>金融科技</td>\n",
       "      <td>顾敏</td>\n",
       "      <td>2014</td>\n",
       "      <td>腾讯、华平投资、淡马锡</td>\n",
       "      <td>粤港澳大湾区</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   排名  企业名称   Company Name  估值（亿人民币）  国家  城市     行业 掌门人/创始人  成立年份  \\\n",
       "0   1  蚂蚁金服  Ant Financial     10000  中国  杭州   金融科技     井贤栋  2014   \n",
       "1   2  字节跳动      Bytedance      5000  中国  北京  媒体和娱乐     张一鸣  2012   \n",
       "2   3  滴滴出行   Didi Chuxing      3600  中国  北京   共享经济      程维  2012   \n",
       "3   6   陆金所          Lufax      2700  中国  上海   金融科技     计葵生  2011   \n",
       "4  11  微众银行         WeBank      1500  中国  深圳   金融科技      顾敏  2014   \n",
       "\n",
       "                   部分投资机构   region  \n",
       "0          春华资本、中投海外、红杉资本  环杭州湾大湾区  \n",
       "1     红杉资本、海纳亚洲、纪源资本、启明创投    渤海大湾区  \n",
       "2  腾讯、阿里巴巴、红杉资本、经纬中国、纪源资本    渤海大湾区  \n",
       "3     摩根士丹利、中银集团、国泰君安（香港）  环杭州湾大湾区  \n",
       "4             腾讯、华平投资、淡马锡   粤港澳大湾区  "
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# B1 20春_pandas_week02_hurun_unicorn.tsv\n",
    "df = pd.read_csv(\"20春_pandas_week02_hurun_unicorn_more.csv\", encoding=\"utf8\", sep=\"\\t\")\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>排名</th>\n",
       "      <th>企业名称</th>\n",
       "      <th>Company Name</th>\n",
       "      <th>估值（亿人民币）</th>\n",
       "      <th>国家</th>\n",
       "      <th>城市</th>\n",
       "      <th>行业</th>\n",
       "      <th>掌门人/创始人</th>\n",
       "      <th>成立年份</th>\n",
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       "      <th>region</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>蚂蚁金服</td>\n",
       "      <td>Ant Financial</td>\n",
       "      <td>10000</td>\n",
       "      <td>中国</td>\n",
       "      <td>杭州</td>\n",
       "      <td>金融科技</td>\n",
       "      <td>井贤栋</td>\n",
       "      <td>2014</td>\n",
       "      <td>春华资本、中投海外、红杉资本</td>\n",
       "      <td>环杭州湾大湾区</td>\n",
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       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>字节跳动</td>\n",
       "      <td>Bytedance</td>\n",
       "      <td>5000</td>\n",
       "      <td>中国</td>\n",
       "      <td>北京</td>\n",
       "      <td>媒体和娱乐</td>\n",
       "      <td>张一鸣</td>\n",
       "      <td>2012</td>\n",
       "      <td>红杉资本、海纳亚洲、纪源资本、启明创投</td>\n",
       "      <td>渤海大湾区</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>滴滴出行</td>\n",
       "      <td>Didi Chuxing</td>\n",
       "      <td>3600</td>\n",
       "      <td>中国</td>\n",
       "      <td>北京</td>\n",
       "      <td>共享经济</td>\n",
       "      <td>程维</td>\n",
       "      <td>2012</td>\n",
       "      <td>腾讯、阿里巴巴、红杉资本、经纬中国、纪源资本</td>\n",
       "      <td>渤海大湾区</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>6</td>\n",
       "      <td>陆金所</td>\n",
       "      <td>Lufax</td>\n",
       "      <td>2700</td>\n",
       "      <td>中国</td>\n",
       "      <td>上海</td>\n",
       "      <td>金融科技</td>\n",
       "      <td>计葵生</td>\n",
       "      <td>2011</td>\n",
       "      <td>摩根士丹利、中银集团、国泰君安（香港）</td>\n",
       "      <td>环杭州湾大湾区</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>11</td>\n",
       "      <td>微众银行</td>\n",
       "      <td>WeBank</td>\n",
       "      <td>1500</td>\n",
       "      <td>中国</td>\n",
       "      <td>深圳</td>\n",
       "      <td>金融科技</td>\n",
       "      <td>顾敏</td>\n",
       "      <td>2014</td>\n",
       "      <td>腾讯、华平投资、淡马锡</td>\n",
       "      <td>粤港澳大湾区</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>5</td>\n",
       "      <td>12</td>\n",
       "      <td>菜鸟网络</td>\n",
       "      <td>Cainiao</td>\n",
       "      <td>1300</td>\n",
       "      <td>中国</td>\n",
       "      <td>杭州</td>\n",
       "      <td>物流</td>\n",
       "      <td>童文红</td>\n",
       "      <td>2013</td>\n",
       "      <td>GIC、淡马锡、春华资本</td>\n",
       "      <td>环杭州湾大湾区</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>6</td>\n",
       "      <td>12</td>\n",
       "      <td>京东数科</td>\n",
       "      <td>JD Digits</td>\n",
       "      <td>1300</td>\n",
       "      <td>中国</td>\n",
       "      <td>北京</td>\n",
       "      <td>金融科技</td>\n",
       "      <td>陈生强</td>\n",
       "      <td>2013</td>\n",
       "      <td>红杉资本、嘉实投资、中国太平</td>\n",
       "      <td>渤海大湾区</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>7</td>\n",
       "      <td>14</td>\n",
       "      <td>快手</td>\n",
       "      <td>Kuaishou</td>\n",
       "      <td>1200</td>\n",
       "      <td>中国</td>\n",
       "      <td>北京</td>\n",
       "      <td>媒体和娱乐</td>\n",
       "      <td>宿华</td>\n",
       "      <td>2011</td>\n",
       "      <td>红杉资本、晨兴资本、百度、腾讯</td>\n",
       "      <td>渤海大湾区</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   排名  企业名称   Company Name  估值（亿人民币）  国家  城市     行业 掌门人/创始人  成立年份  \\\n",
       "0   1  蚂蚁金服  Ant Financial     10000  中国  杭州   金融科技     井贤栋  2014   \n",
       "1   2  字节跳动      Bytedance      5000  中国  北京  媒体和娱乐     张一鸣  2012   \n",
       "2   3  滴滴出行   Didi Chuxing      3600  中国  北京   共享经济      程维  2012   \n",
       "3   6   陆金所          Lufax      2700  中国  上海   金融科技     计葵生  2011   \n",
       "4  11  微众银行         WeBank      1500  中国  深圳   金融科技      顾敏  2014   \n",
       "5  12  菜鸟网络        Cainiao      1300  中国  杭州     物流     童文红  2013   \n",
       "6  12  京东数科      JD Digits      1300  中国  北京   金融科技     陈生强  2013   \n",
       "7  14    快手       Kuaishou      1200  中国  北京  媒体和娱乐      宿华  2011   \n",
       "\n",
       "                   部分投资机构   region  \n",
       "0          春华资本、中投海外、红杉资本  环杭州湾大湾区  \n",
       "1     红杉资本、海纳亚洲、纪源资本、启明创投    渤海大湾区  \n",
       "2  腾讯、阿里巴巴、红杉资本、经纬中国、纪源资本    渤海大湾区  \n",
       "3     摩根士丹利、中银集团、国泰君安（香港）  环杭州湾大湾区  \n",
       "4             腾讯、华平投资、淡马锡   粤港澳大湾区  \n",
       "5            GIC、淡马锡、春华资本  环杭州湾大湾区  \n",
       "6          红杉资本、嘉实投资、中国太平    渤海大湾区  \n",
       "7         红杉资本、晨兴资本、百度、腾讯    渤海大湾区  "
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# B2 20春_pandas_week02_hurun_unicorn_more.csv\n",
    "df = pd.read_csv(\"20春_pandas_week02_hurun_unicorn_more.csv\", encoding=\"utf8\", sep=\"\\t\")\n",
    "df.head(8)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>排名</th>\n",
       "      <th>企业名称</th>\n",
       "      <th>Company Name</th>\n",
       "      <th>估值（亿人民币）</th>\n",
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       "      <th>region</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>196</td>\n",
       "      <td>264</td>\n",
       "      <td>越海全球</td>\n",
       "      <td>YH Global</td>\n",
       "      <td>70</td>\n",
       "      <td>中国</td>\n",
       "      <td>深圳</td>\n",
       "      <td>物流</td>\n",
       "      <td>张泉</td>\n",
       "      <td>2012</td>\n",
       "      <td>涌铧投资、汇能金融、磐石资本</td>\n",
       "      <td>粤港澳大湾区</td>\n",
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       "      <td>197</td>\n",
       "      <td>264</td>\n",
       "      <td>一点资讯</td>\n",
       "      <td>Yidianzixun</td>\n",
       "      <td>70</td>\n",
       "      <td>中国</td>\n",
       "      <td>北京</td>\n",
       "      <td>媒体和娱乐</td>\n",
       "      <td>李亚</td>\n",
       "      <td>2012</td>\n",
       "      <td>凤凰、小米、IDG</td>\n",
       "      <td>渤海大湾区</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>198</td>\n",
       "      <td>264</td>\n",
       "      <td>易久批</td>\n",
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       "      <td>70</td>\n",
       "      <td>中国</td>\n",
       "      <td>北京</td>\n",
       "      <td>电子商务</td>\n",
       "      <td>王朝成</td>\n",
       "      <td>2014</td>\n",
       "      <td>美团点评、腾讯、贝塔斯曼</td>\n",
       "      <td>渤海大湾区</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>199</td>\n",
       "      <td>264</td>\n",
       "      <td>壹米滴答</td>\n",
       "      <td>Yimidida</td>\n",
       "      <td>70</td>\n",
       "      <td>中国</td>\n",
       "      <td>上海</td>\n",
       "      <td>物流</td>\n",
       "      <td>杨兴运</td>\n",
       "      <td>2015</td>\n",
       "      <td>博裕资本、厚朴投资、普洛斯、源码资本、鼎晖投资</td>\n",
       "      <td>环杭州湾大湾区</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>200</td>\n",
       "      <td>264</td>\n",
       "      <td>洋码头</td>\n",
       "      <td>yMatou</td>\n",
       "      <td>70</td>\n",
       "      <td>中国</td>\n",
       "      <td>上海</td>\n",
       "      <td>电子商务</td>\n",
       "      <td>曾碧波</td>\n",
       "      <td>2009</td>\n",
       "      <td>远镜创投、赛富基金</td>\n",
       "      <td>环杭州湾大湾区</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>201</td>\n",
       "      <td>264</td>\n",
       "      <td>有利网</td>\n",
       "      <td>Yooli</td>\n",
       "      <td>70</td>\n",
       "      <td>中国</td>\n",
       "      <td>北京</td>\n",
       "      <td>金融科技</td>\n",
       "      <td>吴逸然</td>\n",
       "      <td>2012</td>\n",
       "      <td>高瓴资本、晨兴资本、软银中国</td>\n",
       "      <td>渤海大湾区</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>202</td>\n",
       "      <td>264</td>\n",
       "      <td>网易有道</td>\n",
       "      <td>Youdao</td>\n",
       "      <td>70</td>\n",
       "      <td>中国</td>\n",
       "      <td>北京</td>\n",
       "      <td>软件与服务</td>\n",
       "      <td>周枫</td>\n",
       "      <td>2007</td>\n",
       "      <td>君联资本、慕华投资</td>\n",
       "      <td>渤海大湾区</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>203</td>\n",
       "      <td>264</td>\n",
       "      <td>云鸟科技</td>\n",
       "      <td>Yunniao</td>\n",
       "      <td>70</td>\n",
       "      <td>中国</td>\n",
       "      <td>北京</td>\n",
       "      <td>物流</td>\n",
       "      <td>韩毅</td>\n",
       "      <td>2014</td>\n",
       "      <td>华平投资、红杉资本、经纬中国</td>\n",
       "      <td>渤海大湾区</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>204</td>\n",
       "      <td>264</td>\n",
       "      <td>掌门1对1</td>\n",
       "      <td>Zhangmen</td>\n",
       "      <td>70</td>\n",
       "      <td>中国</td>\n",
       "      <td>上海</td>\n",
       "      <td>教育科技</td>\n",
       "      <td>张翼</td>\n",
       "      <td>2014</td>\n",
       "      <td>顺为资本、达晨创投、华平投资</td>\n",
       "      <td>环杭州湾大湾区</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>205</td>\n",
       "      <td>264</td>\n",
       "      <td>转转</td>\n",
       "      <td>Zhuanzhuan</td>\n",
       "      <td>70</td>\n",
       "      <td>中国</td>\n",
       "      <td>北京</td>\n",
       "      <td>电子商务</td>\n",
       "      <td>姚劲波</td>\n",
       "      <td>2015</td>\n",
       "      <td>腾讯</td>\n",
       "      <td>渤海大湾区</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      排名   企业名称 Company Name  估值（亿人民币）  国家  城市     行业 掌门人/创始人  成立年份  \\\n",
       "196  264   越海全球    YH Global        70  中国  深圳     物流      张泉  2012   \n",
       "197  264   一点资讯  Yidianzixun        70  中国  北京  媒体和娱乐      李亚  2012   \n",
       "198  264    易久批      Yijiupi        70  中国  北京   电子商务     王朝成  2014   \n",
       "199  264   壹米滴答     Yimidida        70  中国  上海     物流     杨兴运  2015   \n",
       "200  264    洋码头       yMatou        70  中国  上海   电子商务     曾碧波  2009   \n",
       "201  264    有利网        Yooli        70  中国  北京   金融科技     吴逸然  2012   \n",
       "202  264   网易有道       Youdao        70  中国  北京  软件与服务      周枫  2007   \n",
       "203  264   云鸟科技      Yunniao        70  中国  北京     物流      韩毅  2014   \n",
       "204  264  掌门1对1     Zhangmen        70  中国  上海   教育科技      张翼  2014   \n",
       "205  264     转转   Zhuanzhuan        70  中国  北京   电子商务     姚劲波  2015   \n",
       "\n",
       "                      部分投资机构   region  \n",
       "196           涌铧投资、汇能金融、磐石资本   粤港澳大湾区  \n",
       "197                凤凰、小米、IDG    渤海大湾区  \n",
       "198             美团点评、腾讯、贝塔斯曼    渤海大湾区  \n",
       "199  博裕资本、厚朴投资、普洛斯、源码资本、鼎晖投资  环杭州湾大湾区  \n",
       "200                远镜创投、赛富基金  环杭州湾大湾区  \n",
       "201           高瓴资本、晨兴资本、软银中国    渤海大湾区  \n",
       "202                君联资本、慕华投资    渤海大湾区  \n",
       "203           华平投资、红杉资本、经纬中国    渤海大湾区  \n",
       "204           顺为资本、达晨创投、华平投资  环杭州湾大湾区  \n",
       "205                       腾讯    渤海大湾区  "
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv(\"20春_pandas_week02_hurun_unicorn_more.csv\", encoding=\"utf8\", sep=\"\\t\")\n",
    "df.tail(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 206 entries, 0 to 205\n",
      "Data columns (total 11 columns):\n",
      "排名              206 non-null int64\n",
      "企业名称            206 non-null object\n",
      "Company Name    206 non-null object\n",
      "估值（亿人民币）        206 non-null int64\n",
      "国家              206 non-null object\n",
      "城市              206 non-null object\n",
      "行业              206 non-null object\n",
      "掌门人/创始人         206 non-null object\n",
      "成立年份            206 non-null int64\n",
      "部分投资机构          206 non-null object\n",
      "region          184 non-null object\n",
      "dtypes: int64(3), object(8)\n",
      "memory usage: 17.8+ KB\n"
     ]
    }
   ],
   "source": [
    "df = pd.read_csv(\"20春_pandas_week02_hurun_unicorn_more.csv\", encoding=\"utf8\", sep=\"\\t\")\n",
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <th></th>\n",
       "      <th>排名</th>\n",
       "      <th>企业名称</th>\n",
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       "      <td>206.000000</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
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       "      <td>200</td>\n",
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       "      <td>201</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>top</td>\n",
       "      <td>NaN</td>\n",
       "      <td>掌门1对1</td>\n",
       "      <td>Suning Sports</td>\n",
       "      <td>NaN</td>\n",
       "      <td>中国</td>\n",
       "      <td>北京</td>\n",
       "      <td>电子商务</td>\n",
       "      <td>张勇</td>\n",
       "      <td>NaN</td>\n",
       "      <td>红杉资本</td>\n",
       "      <td>渤海大湾区</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>freq</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>206</td>\n",
       "      <td>82</td>\n",
       "      <td>33</td>\n",
       "      <td>3</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3</td>\n",
       "      <td>86</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>mean</td>\n",
       "      <td>191.296117</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>265.533981</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2012.009709</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>std</td>\n",
       "      <td>89.178170</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>842.508714</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.580421</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>min</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>70.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2000.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>25%</td>\n",
       "      <td>84.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>70.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2010.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>50%</td>\n",
       "      <td>224.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2013.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>75%</td>\n",
       "      <td>264.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>200.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2014.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>max</td>\n",
       "      <td>264.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>10000.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2019.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                排名   企业名称   Company Name      估值（亿人民币）   国家   城市    行业  \\\n",
       "count   206.000000    206            206    206.000000  206  206   206   \n",
       "unique         NaN    206            206           NaN    1   18    21   \n",
       "top            NaN  掌门1对1  Suning Sports           NaN   中国   北京  电子商务   \n",
       "freq           NaN      1              1           NaN  206   82    33   \n",
       "mean    191.296117    NaN            NaN    265.533981  NaN  NaN   NaN   \n",
       "std      89.178170    NaN            NaN    842.508714  NaN  NaN   NaN   \n",
       "min       1.000000    NaN            NaN     70.000000  NaN  NaN   NaN   \n",
       "25%      84.000000    NaN            NaN     70.000000  NaN  NaN   NaN   \n",
       "50%     224.000000    NaN            NaN    100.000000  NaN  NaN   NaN   \n",
       "75%     264.000000    NaN            NaN    200.000000  NaN  NaN   NaN   \n",
       "max     264.000000    NaN            NaN  10000.000000  NaN  NaN   NaN   \n",
       "\n",
       "       掌门人/创始人         成立年份 部分投资机构 region  \n",
       "count      206   206.000000    206    184  \n",
       "unique     200          NaN    201      3  \n",
       "top         张勇          NaN   红杉资本  渤海大湾区  \n",
       "freq         3          NaN      3     86  \n",
       "mean       NaN  2012.009709    NaN    NaN  \n",
       "std        NaN     3.580421    NaN    NaN  \n",
       "min        NaN  2000.000000    NaN    NaN  \n",
       "25%        NaN  2010.000000    NaN    NaN  \n",
       "50%        NaN  2013.000000    NaN    NaN  \n",
       "75%        NaN  2014.000000    NaN    NaN  \n",
       "max        NaN  2019.000000    NaN    NaN  "
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv(\"20春_pandas_week02_hurun_unicorn_more.csv\", encoding=\"utf8\", sep=\"\\t\")\n",
    "df.describe(include=\"all\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![02_io_readwrite.svg](https://pandas.pydata.org/pandas-docs/version/1.0.2/_images/02_io_readwrite.svg)\n",
    "### 读读读的小结\n",
    "1. pandas支持多种格式和数据源只要前面有read_*即可，也可输出多种语言，使用df.to_html()\n",
    "2. head()默认读5行，head(n)都前n行\n",
    "3. pandas还提供了一种 tail(n)方法，读最后n行\n",
    "\n",
    "\n",
    "----- \n",
    "----- "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![03_subset_columns_rows.svg](https://pandas.pydata.org/pandas-docs/version/1.0.2/_images/03_subset_columns_rows.svg)\n",
    "## 如何选择表格的子集？  \n",
    "\n",
    "切切切，**切片** (英文叫slice) 是数据科学家找突破点的重要工具，是她们的数据解剖刀...\n",
    "\n",
    "* 要切单个列，请使用方括号[]和感兴趣的列的列名。\n",
    "* 每一列DataFrame都是一个Series。选择单列后，返回的对象是pandas DataFrame。\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "行（列）选取（单维度选取）：df[]。这种情况一次只能选取行或者列，即一次选取中，只能为行或者列设置筛选条件（只能为一个维度设置筛选条件）。\n",
    "\n",
    "区域选取（多维选取）：df.loc[]，df.iloc[]，df.ix[]。这种方式可以同时为多个维度设置筛选条件。\n",
    "\n",
    "单元格选取（点选取）：df.at[]，df.iat[]。准确定位一个单元格。\n",
    "\n",
    "\n",
    "####  列子集\n",
    "```python\n",
    "一次性创建了行和列的子集，仅使用选择括号[]已不再足够。的 loc/ iloc经营者必须在选择括号的前面[]。使用loc/时iloc，逗号前面的部分是想要的行，逗号后面的部分是想要选择的列。\n",
    "df.loc()使用行和列名称时选择特定的行和/或列\n",
    "df.iloc()只使用表格中的位置时选择特定的行和/或列\n",
    "df.head(n)\n",
    "df.tail(n)\n",
    "df.nlargest(n, '变量')\n",
    "df.nsmallest(n, '变量')\n",
    "df[df.估值（亿人民币）> 10]\n",
    "```\n",
    "\n",
    "#### 行子集\n",
    "```python\n",
    "\n",
    "#  行子集\n",
    "df[['变量X','变量Y','变量Z']]\n",
    "df[['变量X']]\n",
    "df['变量X']\n",
    "```\n",
    "\n",
    "#### 列+行子集\n",
    "```python\n",
    "\n",
    "#  行子集\n",
    "df.loc[:,['变量X':'变量Z']]    # 注意中括号里的: 和 ,的使用\n",
    "df.iloc[:,[1,2,5]]\n",
    "df.loc[df['变量X']>10, ['变量X','变量Z'] ]   \n",
    "```\n",
    "\n",
    "\n",
    "-----"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>0</td>\n",
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       "      <td>4</td>\n",
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       "      <td>1500</td>\n",
       "      <td>中国</td>\n",
       "      <td>深圳</td>\n",
       "      <td>金融科技</td>\n",
       "      <td>顾敏</td>\n",
       "      <td>2014</td>\n",
       "      <td>腾讯、华平投资、淡马锡</td>\n",
       "      <td>粤港澳大湾区</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>5</td>\n",
       "      <td>12</td>\n",
       "      <td>菜鸟网络</td>\n",
       "      <td>Cainiao</td>\n",
       "      <td>1300</td>\n",
       "      <td>中国</td>\n",
       "      <td>杭州</td>\n",
       "      <td>物流</td>\n",
       "      <td>童文红</td>\n",
       "      <td>2013</td>\n",
       "      <td>GIC、淡马锡、春华资本</td>\n",
       "      <td>环杭州湾大湾区</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>6</td>\n",
       "      <td>12</td>\n",
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       "      <td>JD Digits</td>\n",
       "      <td>1300</td>\n",
       "      <td>中国</td>\n",
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       "      <td>渤海大湾区</td>\n",
       "    </tr>\n",
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       "      <td>9</td>\n",
       "      <td>20</td>\n",
       "      <td>比特大陆</td>\n",
       "      <td>Bitmain</td>\n",
       "      <td>800</td>\n",
       "      <td>中国</td>\n",
       "      <td>北京</td>\n",
       "      <td>区块链</td>\n",
       "      <td>詹克团，吴忌寒</td>\n",
       "      <td>2013</td>\n",
       "      <td>红杉资本、IDG、Crimson Ventures, 创新工场</td>\n",
       "      <td>渤海大湾区</td>\n",
       "    </tr>\n",
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       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>198</td>\n",
       "      <td>264</td>\n",
       "      <td>易久批</td>\n",
       "      <td>Yijiupi</td>\n",
       "      <td>70</td>\n",
       "      <td>中国</td>\n",
       "      <td>北京</td>\n",
       "      <td>电子商务</td>\n",
       "      <td>王朝成</td>\n",
       "      <td>2014</td>\n",
       "      <td>美团点评、腾讯、贝塔斯曼</td>\n",
       "      <td>渤海大湾区</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>199</td>\n",
       "      <td>264</td>\n",
       "      <td>壹米滴答</td>\n",
       "      <td>Yimidida</td>\n",
       "      <td>70</td>\n",
       "      <td>中国</td>\n",
       "      <td>上海</td>\n",
       "      <td>物流</td>\n",
       "      <td>杨兴运</td>\n",
       "      <td>2015</td>\n",
       "      <td>博裕资本、厚朴投资、普洛斯、源码资本、鼎晖投资</td>\n",
       "      <td>环杭州湾大湾区</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>203</td>\n",
       "      <td>264</td>\n",
       "      <td>云鸟科技</td>\n",
       "      <td>Yunniao</td>\n",
       "      <td>70</td>\n",
       "      <td>中国</td>\n",
       "      <td>北京</td>\n",
       "      <td>物流</td>\n",
       "      <td>韩毅</td>\n",
       "      <td>2014</td>\n",
       "      <td>华平投资、红杉资本、经纬中国</td>\n",
       "      <td>渤海大湾区</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>204</td>\n",
       "      <td>264</td>\n",
       "      <td>掌门1对1</td>\n",
       "      <td>Zhangmen</td>\n",
       "      <td>70</td>\n",
       "      <td>中国</td>\n",
       "      <td>上海</td>\n",
       "      <td>教育科技</td>\n",
       "      <td>张翼</td>\n",
       "      <td>2014</td>\n",
       "      <td>顺为资本、达晨创投、华平投资</td>\n",
       "      <td>环杭州湾大湾区</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>205</td>\n",
       "      <td>264</td>\n",
       "      <td>转转</td>\n",
       "      <td>Zhuanzhuan</td>\n",
       "      <td>70</td>\n",
       "      <td>中国</td>\n",
       "      <td>北京</td>\n",
       "      <td>电子商务</td>\n",
       "      <td>姚劲波</td>\n",
       "      <td>2015</td>\n",
       "      <td>腾讯</td>\n",
       "      <td>渤海大湾区</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>104 rows × 11 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      排名   企业名称   Company Name  估值（亿人民币）  国家  城市    行业  掌门人/创始人  成立年份  \\\n",
       "0      1   蚂蚁金服  Ant Financial     10000  中国  杭州  金融科技      井贤栋  2014   \n",
       "4     11   微众银行         WeBank      1500  中国  深圳  金融科技       顾敏  2014   \n",
       "5     12   菜鸟网络        Cainiao      1300  中国  杭州    物流      童文红  2013   \n",
       "6     12   京东数科      JD Digits      1300  中国  北京  金融科技      陈生强  2013   \n",
       "9     20   比特大陆        Bitmain       800  中国  北京   区块链  詹克团，吴忌寒  2013   \n",
       "..   ...    ...            ...       ...  ..  ..   ...      ...   ...   \n",
       "198  264    易久批        Yijiupi        70  中国  北京  电子商务      王朝成  2014   \n",
       "199  264   壹米滴答       Yimidida        70  中国  上海    物流      杨兴运  2015   \n",
       "203  264   云鸟科技        Yunniao        70  中国  北京    物流       韩毅  2014   \n",
       "204  264  掌门1对1       Zhangmen        70  中国  上海  教育科技       张翼  2014   \n",
       "205  264     转转     Zhuanzhuan        70  中国  北京  电子商务      姚劲波  2015   \n",
       "\n",
       "                              部分投资机构   region  \n",
       "0                     春华资本、中投海外、红杉资本  环杭州湾大湾区  \n",
       "4                        腾讯、华平投资、淡马锡   粤港澳大湾区  \n",
       "5                       GIC、淡马锡、春华资本  环杭州湾大湾区  \n",
       "6                     红杉资本、嘉实投资、中国太平    渤海大湾区  \n",
       "9    红杉资本、IDG、Crimson Ventures, 创新工场    渤海大湾区  \n",
       "..                               ...      ...  \n",
       "198                     美团点评、腾讯、贝塔斯曼    渤海大湾区  \n",
       "199          博裕资本、厚朴投资、普洛斯、源码资本、鼎晖投资  环杭州湾大湾区  \n",
       "203                   华平投资、红杉资本、经纬中国    渤海大湾区  \n",
       "204                   顺为资本、达晨创投、华平投资  环杭州湾大湾区  \n",
       "205                               腾讯    渤海大湾区  \n",
       "\n",
       "[104 rows x 11 columns]"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[df[\"成立年份\"]> 2012]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![03_subset_columns_rows.svg](https://pandas.pydata.org/pandas-docs/version/1.0.2/_images/03_subset_columns_rows.svg)\n",
    "### 切切切的小结\n",
    "<div class=\"emoticon\">😃😄😁</div>\n",
    "\n",
    "1.选取某一整行（多个整行）或某一整列（多个整列）数据时，可以用df[]、df.loc[]、df.iloc[]\n",
    "2.进行区域选取时，如果只能用标签索引，则使用df.loc[]或df.ix[]，如果只能用整数索引，则用df.iloc[]或df.ix[]。\n",
    "3.如果选取单元格，则df.at[]、df.iat[]、df.loc[]、df.iloc[]都可以，不过要注意参数。\n",
    "4.df[]的方式只能选取行和列数据，不能精确到单元格，所以df[]的返回值一定DataFrame或Series对象。\n",
    "5.选取数据时，返回值存在以下情况：\n",
    "\n",
    "    如果返回值包括单行多列或多行单列时，返回值为Series对象；\n",
    "    如果返回值包括多行多列时，返回值为DataFrame对象；\n",
    "    如果返回值仅为一个单元格（单行单列）时，返回值为基本数据类型，例如str，int等。\n",
    "\n",
    "\n",
    "----- \n",
    "----- "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![04_plot_overview.svg](https://pandas.pydata.org/pandas-docs/version/1.0.2/_images/04_plot_overview.svg)\n",
    "## 如何在熊猫中绘图？\n",
    "\n",
    "> <mark>绘绘绘</mark>，**绘图** ( 数据框.plot() ) 是数据科学家**以数据框为中心**的代码实践，减少以图表类型为开头的编程思维来作图"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 绘绘绘的代码片语\n",
    "基本绘图：plot\n",
    "plotSeries和DataFrame上的方法只是一个简单的包装 plt.plot()：\n",
    "\n",
    "```python\n",
    "df.plot()\n",
    "```\n",
    "\n",
    "在DataFrame上，plot()可以方便地绘制带有标签的所有列：\n",
    "可以使用DataFrame.plot.hist()和Series.plot.hist()方法绘制直方图。\n",
    "要获取水平条形图，请使用barh方法：\n",
    "要生成堆叠的条形图，请传递stacked=True：\n",
    "调用DataFrame的plot.bar()方法会产生多条图：\n",
    "DataFrame.hist() 在多个子图上绘制列的直方图：\n",
    "可以使用Series.plot.box()和绘制箱线图DataFrame.plot.box()，或者DataFrame.boxplot()可视化每列中值的分布。\n",
    "\n",
    "-----"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1321de9b5f8>"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df[[\"估值（亿人民币）\"]].plot(kind=\"bar\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1321fedc6a0>"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df[[\"排名\"]].plot(kind=\"bar\",stacked=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x132201069b0>"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df[[\"估值（亿人民币）\"]].plot(kind=\"area\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 使用matplotlib直接绘图\n",
    "在某些情况下，直接或直接使用matplotlib制作图可能仍然是更可取或必要的，例如，当熊猫尚未支持某种类型的图或自定义时。Series和DataFrame 对象的行为类似于数组，因此可以直接传递给matplotlib函数，而无需显式强制转换。\n",
    "\n",
    "熊猫还自动注册识别日期索引的格式化程序和定位器，从而将日期和时间支持扩展到matplotlib中几乎所有可用的绘图类型。尽管这种格式不能提供与通过熊猫进行绘制时相同的精细度，但是在绘制大量点时可以更快。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1321f2e8080>"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "import matplotlib as mpl  \n",
    "mpl.rcParams['font.sans-serif']=['SimHei'] #用来正常显示中文标签  \n",
    "mpl.rcParams['axes.unicode_minus']=False #用来正常显示负号 \n",
    "\n",
    "df.loc[[0,1,2,3,4,5],[\"估值（亿人民币）\"]].plot(kind=\"bar\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![04_plot_overview.svg](https://pandas.pydata.org/pandas-docs/version/1.0.2/_images/04_plot_overview.svg)\n",
    "### 绘绘绘的小结\n",
    "\n",
    "1. 对于DataFrame，每一行可以看做一个Series,每一列也可以看做一个Series。\n",
    "2. 想利用pandas绘图，可得到Series或DataFrame对象，并利用series.plot()或dataframe.plot()进行绘图\n",
    "\n",
    "<div class=\"emoticon\">😃😄😁</div>\n",
    "\n",
    "----- \n",
    "----- "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![05_newcolumn_2.svg](https://pandas.pydata.org/pandas-docs/version/1.0.2/_images/05_newcolumn_2.svg)\n",
    "## 如何从现有列创建派生新列？\n",
    "> <mark>列列列</mark>，派生新列意谓着变数variables的进一部转换，是数据科学家按步就班做ETL的过程，新派生列就是**变数variables**的转换\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 列列列的代码片语\n",
    "列列列的代码片语 (code snippets)，新手请认真记忆，**注意标点及缩进**\n",
    "\n",
    "```python\n",
    "\n",
    "df['新变量'] = df['变量X'] + df['变量Y']\n",
    "df['新变量'] = [ 转换(x) for x in df['变量Y'] ]     # 列表推导转换\n",
    "\n",
    "```\n",
    "\n",
    "代码片语说明\n",
    "\n",
    "-----"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![05_newcolumn_2.svg](https://pandas.pydata.org/pandas-docs/version/1.0.2/_images/05_newcolumn_2.svg)\n",
    "### 列列列的小结\n",
    "\n",
    "1. 通过将输出分配给DataFrame并在之间使用新列名来创建新列[]。\n",
    "2. 操作是基于元素的，无需遍历行。\n",
    "\n",
    "\n",
    "<div class=\"emoticon\">😃😄😁</div>\n",
    "\n",
    "----- \n",
    "----- "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![06_groupby.svg](https://pandas.pydata.org/pandas-docs/version/1.0.2/_images/06_groupby.svg)\n",
    "\n",
    "## 如何计算汇总描述性统计信息？\n",
    "算算算，描述性统计竟然代码可以这麽容易....，但难的仍是在数据科学家的数据定义及解释上\n",
    "\n",
    "可以使用不同的统计信息，并且可以将其应用于具有数字数据的列。通常，操作会排除丢失的数据，并且默认情况下会跨行进行操作。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 算算算的代码片语\n",
    "\n",
    "\n",
    "```python\n",
    "\n",
    "df.describe()描述统计能够一次性得出数据框所有数值型特征的非空值数目、均值、四分位数、标准差。\n",
    "df.describe(include=all)\n",
    "\n",
    "df.count()非空值数目\n",
    "df.sum()\n",
    "\n",
    "df.min()最小值\n",
    "df.max()最大\n",
    "df.mean()均值\n",
    "df.median()中位数\n",
    "\n",
    "df.var()方差\n",
    "df.std()标准差\n",
    "```\n",
    "\n",
    "代码片语说明\n",
    "\n",
    "-----"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![06_groupby.svg](https://pandas.pydata.org/pandas-docs/version/1.0.2/_images/06_groupby.svg)\n",
    "### 算算算的小结\n",
    "\n",
    "1.describe()描述统计能够一次性得出数据框所有数值型特征的非空值数目、均值、四分位数、标准差。\n",
    "2.从 Series 中提取单个值，或从 DataFrame 的行或列中提取一个 Series。\n",
    "\n",
    "<div class=\"emoticon\">😃😄😁</div>\n",
    "\n",
    "----- \n",
    "----- "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 本周我的总结"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* 这周我学习了pandas处理数据，包括读取数据，生成数据表，从表中查找抽取自己想要部分，并学会了简单的matplotlib绘图方法。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.3"
  }
 },
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